Object-Motion-Aware Grasp Pose Refinement for Stable and Collision-Free Real-Robot Grasping

Abstract

Neural-network-based grasp detection has achieved remarkable success in robotic manipulation due to its efficiency and generalization ability. However, detected poses are often not optimized, leading to undesired object motion or collisions during physical execution. This paper proposes a motion-aware refinement framework that minimizes estimated object motion while enforcing collision avoidance. The seven-dimensional pose is decomposed into approach direction, engagement depth, planar projection, and gripper opening width, enabling efficient and interpretable optimization in lower-dimensional subspaces. To evaluate grasp stability beyond conventional success metrics, we introduce the observed success rate (OSR) together with quantitative motion measurements including translation, rotation, and tilt. Real-robot experiments show that, for high-profile objects, the full pipeline improves the measured success rate (MSR) from 93.33% to 100% and OSR from 83.33% to 97.78%. It also reduces the mean translation from <inline-formula> <tex-math notation="LaTeX">$6.099{\,}mm$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$2.684{\,}mm$ </tex-math></inline-formula>, rotation from 3.732° to 1.344°, and tilt from <inline-formula> <tex-math notation="LaTeX">$4.417{\,}mm$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$1.313{\,}mm$ </tex-math></inline-formula>, while requiring <inline-formula> <tex-math notation="LaTeX">$0.82\pm 0.42{\,}s$ </tex-math></inline-formula> on average. For low-profile objects that cannot be detected by the baseline point-cloud-based planner, the full pipeline achieves 100% MSR and OSR.